Enhanced Particle Swarm Optimization Algorithm

In this article, we present an enhanced version of the Particle Swarm Optimization (PSO) algorithm. This optimization technique simulates collective behavior in biological populations, where solutions (particles) iteratively explore the search space to converge toward optimal results. We provide a comprehensive breakdown of the algorithm's underlying principles, implementation workflow, and key advantages over standard PSO. The discussion includes practical applications with case studies demonstrating problem-solving methodologies, supported by code snippets illustrating critical components such as velocity updates (using inertia weights and acceleration coefficients) and position adjustments via vector operations. Additionally, we analyze the algorithm's limitations—including premature convergence and parameter sensitivity—and outline future research directions for adaptive parameter tuning and hybridization with other optimization techniques. This enables readers to assess its suitability for various engineering and data science applications.